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D-MASC: A Novel Search Strategy for Detecting Regions of Interest in Linear Parameter Space

  • Daniyal KazempourEmail author
  • Kevin Bein
  • Peer Kröger
  • Thomas Seidl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11223)

Abstract

The parameter space transform has been utilized over decades in context of edge detection in the computer vision domain. However the usage of the parameter space transform in context of clustering is a more recent application with the purpose of detecting (hyper)linear correlated clusters. The runtime for detecting edges or hyperlinear correlations can be very high. The contribution of our work is to provide a novel search strategy in order to accelerate the detection of regions of interest in parameter space serving as a foundation for faster detection of edges and linear correlated clusters.

Keywords

Parameter space Clustering Hough transform 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Daniyal Kazempour
    • 1
    Email author
  • Kevin Bein
    • 1
  • Peer Kröger
    • 1
  • Thomas Seidl
    • 1
  1. 1.Ludwig-Maximilians-Universität MünchenMunichGermany

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